
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
import plotly.express as px
from matplotlib.pyplot import *
import json

# import geopandas as gpd
import plotly.express as px

from sklearn.preprocessing import OrdinalEncoder

import warnings
warnings.filterwarnings('ignore')


url_business="C:\\Users\\karla\\Documents\\CUNEF\\5_machine_learning\\2_practicas\\entregables\\Yelp_ML\\00_data\\00_raw\\yelp_academic_dataset_business.json"

df_business=pd.read_json(url_business,
                         lines=True,
                         orient='columns')


print('The shape of the dataset of business contains', str(df_business.shape[0]),'rows and ',str(df_business.shape[1]),\
      ' columns')
print('Here is a list of the first 5 rows of the dataset')
df_business.head(5)

The shape of the dataset of business contains 150346 rows and  14  columns
Here is a list of the first 5 rows of the dataset


df_business.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 150346 entries, 0 to 150345
Data columns (total 14 columns):
 #   Column        Non-Null Count   Dtype  
---  ------        --------------   -----  
 0   business_id   150346 non-null  object 
 1   name          150346 non-null  object 
 2   address       150346 non-null  object 
 3   city          150346 non-null  object 
 4   state         150346 non-null  object 
 5   postal_code   150346 non-null  object 
 6   latitude      150346 non-null  float64
 7   longitude     150346 non-null  float64
 8   stars         150346 non-null  float64
 9   review_count  150346 non-null  int64  
 10  is_open       150346 non-null  int64  
 11  attributes    136602 non-null  object 
 12  categories    150243 non-null  object 
 13  hours         127123 non-null  object 
dtypes: float64(3), int64(2), object(9)
memory usage: 16.1+ MB


# Crear una lista vacia con los nuevos valores que van a estar formando el dataset
chunks = []
size = 1000

# Specify dtypes 
r_dtypes = {"stars": np.float16, 
            "useful": np.int32, 
            "funny": np.int32,
            "cool": np.int32} 

reader = pd.read_json("C:\\Users\\karla\\Documents\\CUNEF\\5_machine_learning\\2_practicas\\entregables\\Yelp_ML\\00_data\\00_raw\\yelp_academic_dataset_review.json",
                      lines=True,
                      chunksize=size,
                      dtype= r_dtypes,
                      orient='records')

for chunk in reader:
        reduced_chunk = chunk.drop(columns=['review_id'])\
                             .query("`date` >= '2006-12-31'")
        chunks.append(reduced_chunk)
        

# Concatenate each chunk into a single dataframe        
df_reviews = pd.concat(chunks, ignore_index=True)


print(f'The size of the dataset is ',str(df_reviews.shape[0]),'columns, with ',str(df_reviews.shape[1]),'rows')
print('Here is a list of the 5 first review information')
df_reviews.head(5)

The size of the dataset is  6985581 columns, with  8 rows
Here is a list of the 5 first review information


df_reviews.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6985581 entries, 0 to 6985580
Data columns (total 8 columns):
 #   Column       Dtype         
---  ------       -----         
 0   user_id      object        
 1   business_id  object        
 2   stars        float16       
 3   useful       int32         
 4   funny        int32         
 5   cool         int32         
 6   text         object        
 7   date         datetime64[ns]
dtypes: datetime64[ns](1), float16(1), int32(3), object(3)
memory usage: 306.5+ MB


url_check= "C:\\Users\\karla\\Documents\\CUNEF\\5_machine_learning\\2_practicas\\entregables\\Yelp_ML\\00_data\\00_raw\\yelp_academic_dataset_checkin.json"

df_checkin=pd.read_json(url_check,
                        lines=True,
                        orient='columns')


print(f'The size of the dataset is ',str(df_checkin.shape[0]),'columns, with ',str(df_checkin.shape[1]),'rows')
print('Here is a list of the 5 first checkin information')
df_checkin.head(5)

The size of the dataset is  131930 columns, with  2 rows
Here is a list of the 5 first checkin information


df_checkin.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 131930 entries, 0 to 131929
Data columns (total 2 columns):
 #   Column       Non-Null Count   Dtype 
---  ------       --------------   ----- 
 0   business_id  131930 non-null  object
 1   date         131930 non-null  object
dtypes: object(2)
memory usage: 2.0+ MB


# Crear una lista vacia con los nuevos valores que van a estar formando el dataset
chunks = []
size = 100
num=0

reader_user = pd.read_json("C:\\Users\\karla\\Documents\\CUNEF\\5_machine_learning\\2_practicas\\entregables\\Yelp_ML\\00_data\\00_raw\\yelp_academic_dataset_user.json",
                      lines=True,
                      chunksize=size,
                      orient='columns')

for chunk in reader_user:
    if (num % 2) == 0:
        chunks.append(chunk)
        num=num+1
    else:
        num=num+1
        
df_user = pd.concat(chunks, ignore_index=True)


print(f'The size of the dataset is ',str(df_user.shape[0]),'columns, with ',str(df_user.shape[1]),'rows')
print('Here is a list of the 5 first User information')
df_user.head(5)

The size of the dataset is  993997 columns, with  22 rows
Here is a list of the 5 first User information


print(f'The size of the dataset is ',str(df_user.shape[0]),'columns, with ',str(df_user.shape[1]),'rows')
print('Here is a list of the 5 first User information')
df_user.head(5)

The size of the dataset is  993997 columns, with  22 rows
Here is a list of the 5 first User information


df_user.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 993997 entries, 0 to 993996
Data columns (total 22 columns):
 #   Column              Non-Null Count   Dtype  
---  ------              --------------   -----  
 0   user_id             993997 non-null  object 
 1   name                993997 non-null  object 
 2   review_count        993997 non-null  int64  
 3   yelping_since       993997 non-null  object 
 4   useful              993997 non-null  int64  
 5   funny               993997 non-null  int64  
 6   cool                993997 non-null  int64  
 7   elite               993997 non-null  object 
 8   friends             993997 non-null  object 
 9   fans                993997 non-null  int64  
 10  average_stars       993997 non-null  float64
 11  compliment_hot      993997 non-null  int64  
 12  compliment_more     993997 non-null  int64  
 13  compliment_profile  993997 non-null  int64  
 14  compliment_cute     993997 non-null  int64  
 15  compliment_list     993997 non-null  int64  
 16  compliment_note     993997 non-null  int64  
 17  compliment_plain    993997 non-null  int64  
 18  compliment_cool     993997 non-null  int64  
 19  compliment_funny    993997 non-null  int64  
 20  compliment_writer   993997 non-null  int64  
 21  compliment_photos   993997 non-null  int64  
dtypes: float64(1), int64(16), object(5)
memory usage: 166.8+ MB


url_tip="C:\\Users\\karla\\Documents\\CUNEF\\5_machine_learning\\2_practicas\\entregables\\Yelp_ML\\00_data\\00_raw\\yelp_academic_dataset_tip.json"

df_tip=pd.read_json(url_tip,
                    lines=True,
                    orient='columns')


print(f'The size of the dataset is ',str(df_tip.shape[0]),'columns, with ',str(df_tip.shape[1]),'rows')
print('Here is a list of the 5 first Tip information')
df_tip.head(5)

The size of the dataset is  908915 columns, with  5 rows
Here is a list of the 5 first Tip information


df_tip.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 908915 entries, 0 to 908914
Data columns (total 5 columns):
 #   Column            Non-Null Count   Dtype         
---  ------            --------------   -----         
 0   user_id           908915 non-null  object        
 1   business_id       908915 non-null  object        
 2   text              908915 non-null  object        
 3   date              908915 non-null  datetime64[ns]
 4   compliment_count  908915 non-null  int64         
dtypes: datetime64[ns](1), int64(1), object(3)
memory usage: 34.7+ MB


# Verify the null values in the dataframe of business
null_col=df_business.isnull().sum().sort_values(ascending=False).rename('null_values').reset_index()

# Create table with all rows and percentage of missing values per variable
null_col['total_rows']=len(df_business)
null_col["miss_percentage"]=round((null_col['null_values']/null_col['total_rows']).mul(100),3)
null_col=null_col[null_col["miss_percentage"]>0]
null_col


# Verify the null values in the dataframe of business
null_col=df_reviews.isnull().sum().sort_values(ascending=False).rename('null_values').reset_index()

# Create table with all rows and percentage of missing values per variable
null_col['total_rows']=len(df_reviews)
null_col["miss_percentage"]=round((null_col['null_values']/null_col['total_rows']).mul(100),3)
null_col=null_col[null_col["miss_percentage"]>0]
null_col


# Verify the null values in the dataframe of business
null_col=df_checkin.isnull().sum().sort_values(ascending=False).rename('null_values').reset_index()

# Create table with all rows and percentage of missing values per variable
null_col['total_rows']=len(df_checkin)
null_col["miss_percentage"]=round((null_col['null_values']/null_col['total_rows']).mul(100),3)
null_col=null_col[null_col["miss_percentage"]>0]
null_col


# Verify the null values in the dataframe of business
null_col=df_user.isnull().sum().sort_values(ascending=False).rename('null_values').reset_index()

# Create table with all rows and percentage of missing values per variable
null_col['total_rows']=len(df_user)
null_col["miss_percentage"]=round((null_col['null_values']/null_col['total_rows']).mul(100),3)
null_col=null_col[null_col["miss_percentage"]>0]
null_col


# Verify the null values in the dataframe of business
null_col=df_tip.isnull().sum().sort_values(ascending=False).rename('null_values').reset_index()

# Create table with all rows and percentage of missing values per variable
null_col['total_rows']=len(df_tip)
null_col["miss_percentage"]=round((null_col['null_values']/null_col['total_rows']).mul(100),3)
null_col=null_col[null_col["miss_percentage"]>0]
null_col


# fill the missing values with 'None' in hours and attribute rows
df_business['hours']=df_business['hours'].fillna('None')
df_business['attributes']=df_business['attributes'].fillna('None')

# Dropping the rows with missing values in category
df_business.dropna(axis = 0, inplace = True)


# Verify the null values in the dataframe of business
null_col=df_business.isnull().sum().sort_values(ascending=False).rename('null_values').reset_index()

# Create table with all rows and percentage of missing values per variable
null_col['total_rows']=len(df_business)
null_col["miss_percentage"]=round((null_col['null_values']/null_col['total_rows']).mul(100),3)
null_col=null_col[null_col["miss_percentage"]>0]
null_col


df_business.shape

(150243, 14)


# Create the new dataframe to verify the most frequent categories
business_categ=(', '.join(df_business['categories'].dropna())) #create a series of all possible categories unique
business_categ=pd.DataFrame(business_categ.split(','),columns=['category']) # divide the series to rows and convert to DataFrame
business_categ=business_categ.value_counts().to_frame().reset_index() #count the number of each category
business_categ.columns=[ 'category','num_business'] #rename the columns

# Plot the created dataset in a graph to visualize better the information
plt.figure(figsize=(15,10))
ax=sns.barplot(data=business_categ.iloc[0:15], y= 'category', x='num_business', palette="crest")
ax.set_ylabel('Category')
ax.set_xlabel('Number of business')
ax.set_title('Top 15 Business Categories in Yelp Database')

# Add the labels to each bar in the  
for p in ax.patches:
    ax.annotate(int(p.get_width()),
                ((p.get_x() + p.get_width()),
                 p.get_y()),
                 xytext=(1, -18),
                fontsize=12,
                color='black',
                textcoords='offset points',
                horizontalalignment='left')   
    
plt.show()


# Plot the graph to see the states with the most reviews written to different business
fig=px.choropleth(df_business[['state','review_count']].groupby('state').sum().reset_index(),
                  locations='state',
                  locationmode="USA-states",
                  scope='usa',
                  color='review_count',
                  color_continuous_scale="Viridis",
                  labels={'review_count':'Number reviews per State'}
                 )

fig.show()


# Describe the location of the file
url_state_label="C://Users//karla//Documents//CUNEF//5_machine_learning//2_practicas//entregables//Yelp_ML//00_data//00_raw//states_hash.json"

# Create the dataframe from the json file
state_label=pd.read_json(url_state_label, typ='series').to_frame().reset_index()
state_label.columns=['state','state_desc']
state_abbr=state_label['state'].to_list() # create a list with all the states abbreviation

# Create a list with all the unique values of state in the dataset
uni_states=df_business['state'].unique()

# Create new list that will save the values that are a US state abbreviation and the states that are not an abbreviation
no_state=[]
state_contain=[]

# Create a loop that goes through our unique values of state abbreviation and identify the values that are not an state
for elements in uni_states:
    if elements in state_abbr:
        state_contain.append(elements)
    else:
        no_state.append(elements)


# The states that are not part of the US are
no_state

['AB', 'XMS']


fig=px.choropleth(df_business[['state','stars']].groupby('state').mean().reset_index(),
                  locations='state',
                  locationmode="USA-states",
                  scope='usa',
                  color='stars',
                  color_continuous_scale="Viridis",
                  labels={'stars':'Average Star range per State'}
                 )

fig.show()


review_series = df_reviews['date'].value_counts()


review_series.resample('M').sum().plot()

<AxesSubplot: >


plt.figure(figsize=(10,5))
ax = sns.countplot( x='stars' , data = df_reviews, palette='crest')
ax.set_ylabel('Count')
ax.set_xlabel('Stars')
ax.set_title('Average Star Rating by Reviews in the Yelp Database')

for p in ax.patches:
        width, height = p.get_width(), p.get_height()
        x, y = p.get_xy() 
        ax.text(x+width-.4, 
                y+height,
                '{:.0f}'.format(height),
                horizontalalignment='center') 
                
plt.show()


plt.figure(figsize=(7,5))
ax = sns.histplot( x='average_stars' , data = df_user, palette='crest', binwidth=0.25)
ax.set_ylabel('Count')
ax.set_xlabel('Stars')
ax.set_title('Average star of assign by users')

for p in ax.patches:
        width, height = p.get_width(), p.get_height()
        x, y = p.get_xy() 
        ax.text(x+width-.4, 
                y+height,
                '{:.0f}'.format(height),
                horizontalalignment='center') 
                
plt.show()


df_new=(', '.join(df_user['friends']))


df_new


plt.figure(figsize=(10,8))

df_user_corr=df_user.copy()
df_user_corr=df_user_corr.drop(columns=['user_id','name','friends','yelping_since','elite','friends'], axis=1)

sns.heatmap(df_user_corr.corr())

<AxesSubplot: >


# Create a copy of primery user dataset andDrop the user_id and the name as this information is not necessary
user_info=df_user.copy()
user_info=user_info.drop(['user_id','name'], axis=1)


# Create a function that will count all the values inside each list that are seperate by a comma
def count_val(column_name):
    num_elements=[]
    for elements in user_info[column_name]:
        if len(elements)==0:
            num_elements.append(0)
        else:
            num_elements.append(len(elements.split(',')))
    return num_elements


# Add the new column to the existing dataset of user_info and after drop the friends columns
user_info['num_friends']=count_val('friends')
user_info['times_elite']=count_val('elite')

# Eliminate the original columns of friends and elite from the dataset
user_info=user_info.drop(['friends','elite'],axis=1)


# Change the year columns to specify only the year on which each user enter the platform
user_info['yelping_since']=pd.to_datetime(user_info['yelping_since'],format='%Y-%m-%d %H:%M:%S') #convert from obj to date
user_info['yelping_since']=user_info['yelping_since'].dt.strftime('%Y').astype('int')
user_info


user_info.shape

(993997, 20)


# Dataset of reviews
# df_reviews['stars']=df_reviews['stars'].astype(float)
# df_reviews.to_parquet("C:/Users/karla/Documents/CUNEF/5_machine_learning/2_practicas/entregables/Yelp_ML/00_data/01_process/reviews_data.parquet")

# Dataset of User_info
user_info['average_stars']=user_info['average_stars'].astype('float') # We do this to be able to save the dataset into a parquet
user_info.to_parquet("C:/Users/karla/Documents/CUNEF/5_machine_learning/2_practicas/entregables/Yelp_ML/00_data/01_process/user_info_data.parquet")

	business_id	name	address	city	state	postal_code	latitude	longitude	stars	review_count	is_open	attributes	categories	hours
0	Pns2l4eNsfO8kk83dixA6A	Abby Rappoport, LAC, CMQ	1616 Chapala St, Ste 2	Santa Barbara	CA	93101	34.426679	-119.711197	5.0	7	0	{'ByAppointmentOnly': 'True'}	Doctors, Traditional Chinese Medicine, Naturop...	None
1	mpf3x-BjTdTEA3yCZrAYPw	The UPS Store	87 Grasso Plaza Shopping Center	Affton	MO	63123	38.551126	-90.335695	3.0	15	1	{'BusinessAcceptsCreditCards': 'True'}	Shipping Centers, Local Services, Notaries, Ma...	{'Monday': '0:0-0:0', 'Tuesday': '8:0-18:30', ...
2	tUFrWirKiKi_TAnsVWINQQ	Target	5255 E Broadway Blvd	Tucson	AZ	85711	32.223236	-110.880452	3.5	22	0	{'BikeParking': 'True', 'BusinessAcceptsCredit...	Department Stores, Shopping, Fashion, Home & G...	{'Monday': '8:0-22:0', 'Tuesday': '8:0-22:0', ...
3	MTSW4McQd7CbVtyjqoe9mw	St Honore Pastries	935 Race St	Philadelphia	PA	19107	39.955505	-75.155564	4.0	80	1	{'RestaurantsDelivery': 'False', 'OutdoorSeati...	Restaurants, Food, Bubble Tea, Coffee & Tea, B...	{'Monday': '7:0-20:0', 'Tuesday': '7:0-20:0', ...
4	mWMc6_wTdE0EUBKIGXDVfA	Perkiomen Valley Brewery	101 Walnut St	Green Lane	PA	18054	40.338183	-75.471659	4.5	13	1	{'BusinessAcceptsCreditCards': 'True', 'Wheelc...	Brewpubs, Breweries, Food	{'Wednesday': '14:0-22:0', 'Thursday': '16:0-2...

	user_id	business_id	stars	useful	cool	text	date
0	mh_-eMZ6K5RLWhZyISBhwA	XQfwVwDr-v0ZS3_CbbE5Xw	3.0	0	0	If you decide to eat here, just be aware it is...	2018-07-07 22:09:11
1	OyoGAe7OKpv6SyGZT5g77Q	7ATYjTIgM3jUlt4UM3IypQ	5.0	1	1	I've taken a lot of spin classes over the year...	2012-01-03 15:28:18
2	8g_iMtfSiwikVnbP2etR0A	YjUWPpI6HXG530lwP-fb2A	3.0	0	0	Family diner. Had the buffet. Eclectic assortm...	2014-02-05 20:30:30
3	_7bHUi9Uuf5__HHc_Q8guQ	kxX2SOes4o-D3ZQBkiMRfA	5.0	1	1	Wow! Yummy, different, delicious. Our favo...	2015-01-04 00:01:03
4	bcjbaE6dDog4jkNY91ncLQ	e4Vwtrqf-wpJfwesgvdgxQ	4.0	1	1	Cute interior and owner (?) gave us tour of up...	2017-01-14 20:54:15

	business_id	date
0	---kPU91CF4Lq2-WlRu9Lw	2020-03-13 21:10:56, 2020-06-02 22:18:06, 2020...
1	--0iUa4sNDFiZFrAdIWhZQ	2010-09-13 21:43:09, 2011-05-04 23:08:15, 2011...
2	--30_8IhuyMHbSOcNWd6DQ	2013-06-14 23:29:17, 2014-08-13 23:20:22
3	--7PUidqRWpRSpXebiyxTg	2011-02-15 17:12:00, 2011-07-28 02:46:10, 2012...
4	--7jw19RH9JKXgFohspgQw	2014-04-21 20:42:11, 2014-04-28 21:04:46, 2014...

	user_id	name	review_count	yelping_since	useful	funny	cool	elite	friends	fans	...	compliment_more	compliment_profile	compliment_cute	compliment_list	compliment_note	compliment_plain	compliment_cool	compliment_funny	compliment_writer	compliment_photos
0	qVc8ODYU5SZjKXVBgXdI7w	Walker	585	2007-01-25 16:47:26	7217	1259	5994	2007	NSCy54eWehBJyZdG2iE84w, pe42u7DcCH2QmI81NX-8qA...	267	...	65	55	56	18	232	844	467	467	239	180
1	j14WgRoU_-2ZE1aw1dXrJg	Daniel	4333	2009-01-25 04:35:42	43091	13066	27281	2009,2010,2011,2012,2013,2014,2015,2016,2017,2...	ueRPE0CX75ePGMqOFVj6IQ, 52oH4DrRvzzl8wh5UXyU0A...	3138	...	264	184	157	251	1847	7054	3131	3131	1521	1946
2	2WnXYQFK0hXEoTxPtV2zvg	Steph	665	2008-07-25 10:41:00	2086	1010	1003	2009,2010,2011,2012,2013	LuO3Bn4f3rlhyHIaNfTlnA, j9B4XdHUhDfTKVecyWQgyA...	52	...	13	10	17	3	66	96	119	119	35	18
3	SZDeASXq7o05mMNLshsdIA	Gwen	224	2005-11-29 04:38:33	512	330	299	2009,2010,2011	enx1vVPnfdNUdPho6PH_wg, 4wOcvMLtU6a9Lslggq74Vg...	28	...	4	1	6	2	12	16	26	26	10	9
4	hA5lMy-EnncsH4JoR-hFGQ	Karen	79	2007-01-05 19:40:59	29	15	7		PBK4q9KEEBHhFvSXCUirIw, 3FWPpM7KU1gXeOM_ZbYMbA...	1	...	1	0	0	0	1	1	0	0	0	0

	user_id	name	review_count	yelping_since	useful	funny	cool	elite	friends	fans	...	compliment_more	compliment_profile	compliment_cute	compliment_list	compliment_note	compliment_plain	compliment_cool	compliment_funny	compliment_writer	compliment_photos
0	qVc8ODYU5SZjKXVBgXdI7w	Walker	585	2007-01-25 16:47:26	7217	1259	5994	2007	NSCy54eWehBJyZdG2iE84w, pe42u7DcCH2QmI81NX-8qA...	267	...	65	55	56	18	232	844	467	467	239	180
1	j14WgRoU_-2ZE1aw1dXrJg	Daniel	4333	2009-01-25 04:35:42	43091	13066	27281	2009,2010,2011,2012,2013,2014,2015,2016,2017,2...	ueRPE0CX75ePGMqOFVj6IQ, 52oH4DrRvzzl8wh5UXyU0A...	3138	...	264	184	157	251	1847	7054	3131	3131	1521	1946
2	2WnXYQFK0hXEoTxPtV2zvg	Steph	665	2008-07-25 10:41:00	2086	1010	1003	2009,2010,2011,2012,2013	LuO3Bn4f3rlhyHIaNfTlnA, j9B4XdHUhDfTKVecyWQgyA...	52	...	13	10	17	3	66	96	119	119	35	18
3	SZDeASXq7o05mMNLshsdIA	Gwen	224	2005-11-29 04:38:33	512	330	299	2009,2010,2011	enx1vVPnfdNUdPho6PH_wg, 4wOcvMLtU6a9Lslggq74Vg...	28	...	4	1	6	2	12	16	26	26	10	9
4	hA5lMy-EnncsH4JoR-hFGQ	Karen	79	2007-01-05 19:40:59	29	15	7		PBK4q9KEEBHhFvSXCUirIw, 3FWPpM7KU1gXeOM_ZbYMbA...	1	...	1	0	0	0	1	1	0	0	0	0

CUNEF¶

Master Universitario en Ciencia de Datos¶

Case of Study:¶

Import the libraries that we are going to use¶

1. LOADING THE DATASETS¶

1.1 Dataset de Business¶

1.2 Dataset de Review¶

1.3 Dataset de Checkin¶

1.4 Dataset de User¶

1.5 Dataset de Tip¶

Summary of Dataset Review¶

2. Analyze the datasets¶

2.1 Missing values¶

2.2 Explore some of the variables of the datasets¶

- Dataset of Business¶

- Dataset of Review¶

- Dataset of Users¶

2.3 Conclusions of the Exploratory Analysis of the Datasets¶

3.Preparation of the new datasets that will be used¶

4. Export the datasets to parquet¶

	user_id	business_id	text	date
0	AGNUgVwnZUey3gcPCJ76iw	3uLgwr0qeCNMjKenHJwPGQ	Avengers time with the ladies.	2012-05-18 02:17:21
1	NBN4MgHP9D3cw--SnauTkA	QoezRbYQncpRqyrLH6Iqjg	They have lots of good deserts and tasty cuban...	2013-02-05 18:35:10
2	-copOvldyKh1qr-vzkDEvw	MYoRNLb5chwjQe3c_k37Gg	It's open even when you think it isn't	2013-08-18 00:56:08
3	FjMQVZjSqY8syIO-53KFKw	hV-bABTK-glh5wj31ps_Jw	Very decent fried chicken	2017-06-27 23:05:38
4	ld0AperBXk1h6UbqmM80zw	_uN0OudeJ3Zl_tf6nxg5ww	Appetizers.. platter special for lunch	2012-10-06 19:43:09

	index	null_values	total_rows	miss_percentage
0	hours	23223	150346	15.446
1	attributes	13744	150346	9.142
2	categories	103	150346	0.069